Triple
T31676705
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tom Zachary |
E808417
|
entity |
| Predicate | 1929Team |
P172410
|
FINISHED |
| Object | New York Yankees |
—
|
NE NERFINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: New York Yankees | Statement: [Tom Zachary, 1929Team, New York Yankees]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 1929Team Context triple: [Tom Zachary, 1929Team, New York Yankees]
-
A.
team2
Indicates that the second team involved in a competitive or collaborative context is associated with a given entity or event.
-
B.
ABAteam
Indicates a relationship where an entity is a team that competes in or is affiliated with the American Basketball Association (ABA).
-
C.
manufacturerTeamFor
Indicates a relationship where a specific team is responsible for manufacturing or producing a particular product or item.
-
D.
foundedTeam
Indicates that an entity established or created a particular team.
-
E.
fictionalTeam
Indicates that the related entities are members of, or otherwise associated with, the same fictional team within a narrative or imaginary context.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f348dcf5d48190ac25b1365ae717a8 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6abe15d5c81909ccf4ce37f78bc43 |
completed | May 3, 2026, 1:58 a.m. |
| PD | Predicate disambiguation | batch_69f6aa20a1588190a53533fc9764efb2 |
completed | May 3, 2026, 1:51 a.m. |
| PDg | Predicate description generation | batch_69f6aaf31a548190b2f792ff4b8c002a |
completed | May 3, 2026, 1:54 a.m. |
Created at: April 30, 2026, 11:03 p.m.